The paper studies the framework of teaching a loss function to a machine learning algorithm (the student model). Inspired from ideas of machine teaching and recent work of "learning to teach" [Fan et al. 18], the paper proposes L2T-DLF framework where a teacher model is jointly trained with a student model. Here, the teacher's goal is to learn a better policy of how to generate dynamic loss functions for the student by accounting for the current state of the student (e.g., training iteration, training error, test error). As shown in Algorithm#1, the teacher/student interaction happens in episodes: (1) the teacher's parameter \theta is fixed in a given episode, (2) the student model is trained end-to-end, and (3) then \theta is updated. In Section 3.3, the paper proposes a gradient-based method to update the parameter of the teacher model.